Sequence analysis of Persuasion Data
This is built upon Erika’s work. Using the TraMineR package for sequence analysis. The first part is analysis of persuader’s sequences.
#install.packages("TraMineR")
library(TraMineR)##
## TraMineR stable version 2.0-12 (Built: 2019-06-22)## Website: http://traminer.unige.ch## Please type 'citation("TraMineR")' for citation information.library(magrittr)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionLoad the data. And plot the frequencies of different states at each step, comparing the donated and not donated groups.
persuader <- read.csv("persuaderOnly.csv", stringsAsFactors = F)
persuader.alphab <- c("emo", "log", "cre", "pro","task", "other")
persuader.seq <- seqdef(persuader, 3:12, alphabet = persuader.alphab)## [>] 6 distinct states appear in the data:## 1 = cre## 2 = emo## 3 = log## 4 = other## 5 = pro## 6 = task## [>] state coding:## [alphabet] [label] [long label]## 1 emo emo emo## 2 log log log## 3 cre cre cre## 4 pro pro pro## 5 task task task## 6 other other other## [>] 652 sequences in the data set## [>] min/max sequence length: 10/10donate.seq <- seqdef(persuader %>% filter(persuader$donate_p==1), 3:12, alphabet = persuader.alphab)## [>] 6 distinct states appear in the data:## 1 = cre## 2 = emo## 3 = log## 4 = other## 5 = pro## 6 = task## [>] state coding:## [alphabet] [label] [long label]## 1 emo emo emo## 2 log log log## 3 cre cre cre## 4 pro pro pro## 5 task task task## 6 other other other## [>] 362 sequences in the data set## [>] min/max sequence length: 10/10notdonate.seq <- seqdef(persuader %>% filter(persuader$donate_p==0), 3:12, alphabet = persuader.alphab)## [>] 6 distinct states appear in the data:## 1 = cre## 2 = emo## 3 = log## 4 = other## 5 = pro## 6 = task## [>] state coding:## [alphabet] [label] [long label]## 1 emo emo emo## 2 log log log## 3 cre cre cre## 4 pro pro pro## 5 task task task## 6 other other other## [>] 290 sequences in the data set## [>] min/max sequence length: 10/10#seqdplot(persuader.seq, group = persuader$donate_p, border = NA)Now, we need to analyse the subsequences of these groups.
#transition <- seqetm(persuader.seq, method = "transition")
#transition
pder.seqe <- seqecreate(persuader.seq)
pder.seqestate <- seqecreate(persuader.seq, tevent = "state")
pder.seqeperiod <- seqecreate(persuader.seq, tevent = "period")
#pder.seqe[1]
#pder.seqestate[1]
#pder.seqeperiod[1]
don.seqe <- seqecreate(donate.seq)
don.seqestate <- seqecreate(donate.seq, tevent = "state")
don.seqeperiod <- seqecreate(donate.seq, tevent = "period")
ndon.seqe <- seqecreate(notdonate.seq)
ndon.seqestate <- seqecreate(notdonate.seq, tevent = "state")
ndon.seqeperiod <- seqecreate(notdonate.seq, tevent = "period")There is also another function to compare subsequence frenquencies from two groups. However, there are two methods to calculate, and I don’t know what they are exactly. But the Chi-square test is less strict than the other one Bonferroni test.
pdersubseq <- seqefsub(pder.seqestate, min.support = 10, constraint = seqeconstraint(count.method = 1, max.gap = 1))
cohort <- factor(persuader$donate_p > 0, labels = c("no donation", "donation"))
discrcohort011 <- seqecmpgroup(pdersubseq, group = cohort, method = "chisq", pvalue.limit = 0.1)
discrcohort011## Subsequence Support p.value statistic index
## 1 (log) 0.68404908 0.004229809 8.182429 4
## 2 (task)-(other) 0.15337423 0.016350211 5.764815 17
## 3 (log)-(task)-(other) 0.02760736 0.030203472 4.697674 55
## 4 (emo)-(cre)-(emo)-(log) 0.01687117 0.037892656 4.309834 83
## 5 (other)-(emo)-(cre)-(emo) 0.01687117 0.037892656 4.309834 88
## 6 (log)-(other) 0.22239264 0.058234484 3.586966 10
## 7 (cre)-(pro)-(task) 0.01533742 0.058702928 3.573656 92
## 8 (log)-(other)-(log)-(other) 0.01533742 0.058702928 3.573656 95
## 9 (pro)-(emo) 0.05828221 0.059678586 3.546294 37
## 10 (other)-(emo)-(cre) 0.03374233 0.061439763 3.498087 48
## 11 (log)-(other)-(log) 0.02914110 0.064150364 3.426695 52
## 12 (cre)-(task)-(other) 0.02147239 0.075162806 3.166512 65
## 13 (task) 0.48466258 0.081365723 3.037408 5
## 14 (log)-(emo)-(log) 0.02760736 0.092856437 2.824143 54
## Freq.no donation Freq.donation Resid.no donation Resid.donation
## 1 0.624137931 0.73204420 -1.233568 1.104098
## 2 0.113793103 0.18508287 -1.721119 1.540479
## 3 0.010344828 0.04143646 -1.769258 1.583565
## 4 0.003448276 0.02762431 -1.759837 1.575133
## 5 0.003448276 0.02762431 -1.759837 1.575133
## 6 0.186206897 0.25138122 -1.306701 1.169556
## 7 0.003448276 0.02486188 -1.634833 1.463249
## 8 0.003448276 0.02486188 -1.634833 1.463249
## 9 0.079310345 0.04143646 1.483310 -1.327628
## 10 0.017241379 0.04696133 -1.529750 1.369195
## 11 0.013793103 0.04143646 -1.531080 1.370385
## 12 0.034482759 0.01104972 1.511988 -1.353297
## 13 0.444827586 0.51657459 -0.974415 0.872145
## 14 0.041379310 0.01657459 1.411503 -1.263359
##
## Computed on 652 event sequences
## Constraint Value
## max.gap 1
## count.method COBJplot(discrcohort011, resid.levels = c(0.1,0.05), rows = 1, cols = 1)
plot(discrcohort011, resid.levels = c(0.1,0.05), rows = 1, cols = 1, ptype = "resid")
pdersubseq <- seqefsub(pder.seqestate, min.support = 10, constraint = seqeconstraint(count.method = 1, max.gap = 3))
cohort <- factor(persuader$donate_p > 0, labels = c("no donation", "donation"))
discrcohort01_1 <- seqecmpgroup(pdersubseq, group = cohort, method = "chisq", pvalue.limit = 0.05)
discrcohort01_1## Subsequence Support p.value
## 1 (log)-(task)-(other) 0.10122699 0.0001038347
## 2 (cre)-(log)-(task)-(other) 0.05828221 0.0015632503
## 3 (cre)-(other)-(other)-(log) 0.02760736 0.0017516704
## 4 (other)-(cre)-(other)-(emo)-(other) 0.04754601 0.0021446018
## 5 (emo)-(log)-(task)-(other) 0.05214724 0.0022392035
## 6 (cre)-(cre)-(log)-(other) 0.02607362 0.0027209950
## 7 (emo)-(cre)-(log)-(task)-(other) 0.01993865 0.0029022130
## 8 (cre)-(other)-(emo)-(other) 0.06901840 0.0030934441
## 9 (other)-(cre)-(other)-(emo) 0.08128834 0.0036720464
## 10 (cre)-(other)-(log) 0.10582822 0.0041465257
## 11 (cre)-(log)-(other) 0.19631902 0.0041905578
## 12 (log) 0.68404908 0.0042298091
## 13 (log)-(other)-(task)-(other) 0.03374233 0.0060839659
## 14 (cre)-(emo)-(log)-(task)-(other) 0.02300613 0.0065562034
## 15 (cre)-(log)-(other)-(task)-(other) 0.02300613 0.0065562034
## 16 (other)-(cre)-(emo)-(log)-(task)-(other) 0.01687117 0.0071896180
## 17 (pro)-(log)-(task)-(other) 0.01687117 0.0071896180
## 18 (emo)-(cre)-(log)-(task) 0.03220859 0.0091336005
## 19 (other)-(emo)-(log)-(task)-(other) 0.03220859 0.0091336005
## 20 (other)-(log)-(other)-(other) 0.04601227 0.0100468778
## 21 (other)-(cre)-(other)-(other)-(log) 0.02147239 0.0101706042
## 22 (log)-(other) 0.40797546 0.0112228743
## 23 (pro)-(other)-(log)-(other) 0.03527607 0.0143703965
## 24 (other)-(cre)-(log)-(task)-(other) 0.03987730 0.0145862101
## 25 (cre)-(pro)-(other) 0.06595092 0.0154626991
## 26 (pro)-(other)-(other) 0.04754601 0.0198709336
## 27 (other)-(cre)-(other)-(other) 0.08128834 0.0198972859
## 28 (other)-(log)-(other) 0.17484663 0.0200766379
## 29 (cre)-(other)-(other) 0.12423313 0.0228243302
## 30 (other)-(cre)-(cre)-(log)-(other) 0.01840491 0.0244506914
## 31 (task)-(other) 0.26533742 0.0262910207
## 32 (other)-(cre)-(pro)-(other) 0.04601227 0.0279447849
## 33 (cre)-(pro)-(log)-(other) 0.02300613 0.0283126764
## 34 (cre)-(other)-(emo) 0.13803681 0.0294454543
## 35 (log)-(task) 0.17484663 0.0342230873
## 36 (cre)-(pro)-(other)-(log) 0.01687117 0.0378926557
## 37 (other)-(emo)-(cre)-(emo)-(cre)-(other) 0.01687117 0.0378926557
## 38 (pro)-(cre)-(other) 0.05674847 0.0424443644
## 39 (cre)-(emo)-(log)-(other)-(emo) 0.02300613 0.0441848949
## 40 (log)-(emo)-(task)-(other) 0.02607362 0.0445875669
## 41 (cre)-(other)-(log)-(other) 0.06441718 0.0472332676
## statistic index Freq.no donation Freq.donation Resid.no donation
## 1 15.065668 84 0.048275862 0.14364641 -2.834171
## 2 10.002533 178 0.024137931 0.08563536 -2.408513
## 3 9.793109 411 0.003448276 0.04696133 -2.476094
## 4 9.421464 233 0.017241379 0.07182320 -2.366742
## 5 9.342332 202 0.020689655 0.07734807 -2.345896
## 6 8.985707 423 0.003448276 0.04419890 -2.386127
## 7 8.867947 597 0.000000000 0.03591160 -2.404622
## 8 8.751518 138 0.034482759 0.09668508 -2.238639
## 9 8.439265 110 0.044827586 0.11049724 -2.177763
## 10 8.218509 80 0.065517241 0.13812155 -2.110190
## 11 8.199342 33 0.144827586 0.23756906 -1.979032
## 12 8.182429 4 0.624137931 0.73204420 -1.233568
## 13 7.525263 338 0.010344828 0.05248619 -2.169107
## 14 7.390698 498 0.003448276 0.03867403 -2.195829
## 15 7.390698 500 0.003448276 0.03867403 -2.195829
## 16 7.224975 735 0.000000000 0.03038674 -2.211931
## 17 7.224975 775 0.000000000 0.03038674 -2.211931
## 18 6.796515 347 0.010344828 0.04972376 -2.074617
## 19 6.796515 355 0.010344828 0.04972376 -2.074617
## 20 6.626566 250 0.020689655 0.06629834 -2.010346
## 21 6.604768 555 0.003448276 0.03591160 -2.094656
## 22 6.429681 10 0.351724138 0.45303867 -1.499736
## 23 5.992062 327 0.013793103 0.05248619 -1.947840
## 24 5.965772 291 0.017241379 0.05801105 -1.930342
## 25 5.862968 152 0.037931034 0.08839779 -1.858039
## 26 5.423198 239 0.024137931 0.06629834 -1.828133
## 27 5.420884 111 0.051724138 0.10497238 -1.765839
## 28 5.405218 39 0.134482759 0.20718232 -1.643854
## 29 5.181826 69 0.089655172 0.15193370 -1.670627
## 30 5.062376 665 0.003448276 0.03038674 -1.877440
## 31 4.936777 22 0.220689655 0.30110497 -1.476045
## 32 4.831471 248 0.024137931 0.06353591 -1.736589
## 33 4.808927 501 0.006896552 0.03591160 -1.808679
## 34 4.741379 54 0.103448276 0.16574586 -1.585381
## 35 4.483524 38 0.137931034 0.20441989 -1.503420
## 36 4.309834 714 0.003448276 0.02762431 -1.759837
## 37 4.309834 746 0.003448276 0.02762431 -1.759837
## 38 4.117396 182 0.034482759 0.07458564 -1.591690
## 39 4.049482 496 0.037931034 0.01104972 1.675670
## 40 4.034171 439 0.010344828 0.03867403 -1.658799
## 41 3.937060 155 0.041379310 0.08287293 -1.545754
## Resid.donation
## 1 2.536710
## 2 2.155726
## 3 2.216214
## 4 2.118340
## 5 2.099681
## 6 2.135690
## 7 2.152244
## 8 2.003682
## 9 1.949195
## 10 1.888715
## 11 1.771322
## 12 1.104098
## 13 1.941448
## 14 1.965365
## 15 1.965365
## 16 1.979777
## 17 1.979777
## 18 1.856875
## 19 1.856875
## 20 1.799349
## 21 1.874811
## 22 1.342330
## 23 1.743404
## 24 1.727742
## 25 1.663028
## 26 1.636261
## 27 1.580505
## 28 1.471322
## 29 1.495286
## 30 1.680392
## 31 1.321126
## 32 1.554325
## 33 1.618848
## 34 1.418987
## 35 1.345628
## 36 1.575133
## 37 1.575133
## 38 1.424633
## 39 -1.499800
## 40 1.484699
## 41 1.383519
##
## Computed on 652 event sequences
## Constraint Value
## max.gap 3
## count.method COBJplot(discrcohort01_1, resid.levels = c(0.05,0.01), rows = 1, cols = 1)
plot(discrcohort01_1, resid.levels = c(0.05,0.01), rows = 1, cols = 1, ptype = "resid")
pdersubseq <- seqefsub(pder.seqe, min.support = 10, constraint = seqeconstraint(count.method = 1, max.gap = 1))
cohort <- factor(persuader$donate_p > 0, labels = c("no donation", "donation"))
discrcohort111 <- seqecmpgroup(pdersubseq, group = cohort, method = "chisq", pvalue.limit = 0.1)
discrcohort111## Subsequence Support p.value statistic index
## 1 (log>task)-(task>other) 0.03374233 0.006083966 7.525263 67
## 2 (emo>cre)-(cre>emo)-(emo>log) 0.01993865 0.015772071 5.828109 96
## 3 (other>log) 0.21625767 0.031811135 4.608646 9
## 4 (cre>pro)-(pro>task) 0.01533742 0.058702928 3.573656 127
## 5 (task>other) 0.18558282 0.058882285 3.568590 13
## 6 (log>emo)-(emo>log) 0.03220859 0.063357417 3.447242 70
## 7 (other>task)-(task>cre) 0.01993865 0.064258029 3.423926 104
## 8 (cre>log)-(log>other) 0.04907975 0.084236765 2.981201 46
## 9 (pro>emo) 0.05981595 0.086792462 2.932881 42
## 10 (other>cre)-(cre>emo)-(emo>cre) 0.02760736 0.091714847 2.844014 78
## 11 (log>other) 0.28680982 0.091827695 2.842038 6
## Freq.no donation Freq.donation Resid.no donation Resid.donation
## 1 0.010344828 0.05248619 -2.169107 1.9414477
## 2 0.003448276 0.03314917 -1.988757 1.7800260
## 3 0.175862069 0.24861878 -1.479269 1.3240123
## 4 0.003448276 0.02486188 -1.634833 1.4632490
## 5 0.151724138 0.21270718 -1.338444 1.1979671
## 6 0.048275862 0.01933702 1.524598 -1.3645833
## 7 0.006896552 0.03038674 -1.572891 1.4078076
## 8 0.031034483 0.06353591 -1.387111 1.2415264
## 9 0.079310345 0.04419890 1.357376 -1.2149123
## 10 0.013793103 0.03867403 -1.415840 1.2672399
## 11 0.251724138 0.31491713 -1.115660 0.9985656
##
## Computed on 652 event sequences
## Constraint Value
## max.gap 1
## count.method COBJplot(discrcohort111, resid.levels = c(0.1,0.05), rows = 1, cols = 1)
plot(discrcohort111, resid.levels = c(0.1,0.05), rows = 1, cols = 1, ptype = "resid")
pdersubseq <- seqefsub(pder.seqe, min.support = 10, constraint = seqeconstraint(count.method = 1, max.gap = 3))
cohort <- factor(persuader$donate_p > 0, labels = c("no donation", "donation"))
discrcohort11_1 <- seqecmpgroup(pdersubseq, group = cohort, method = "chisq", pvalue.limit = 0.05)
discrcohort11_1## Subsequence Support p.value
## 1 (other)-(emo>cre) 0.04447853 0.01444164
## 2 (other)-(other>cre)-(cre>other)-(other>emo) 0.01993865 0.01577207
## 3 (emo>cre)-(cre>emo)-(emo>log) 0.03834356 0.02109198
## 4 (log>task)-(task>other) 0.04141104 0.02932134
## 5 (other>cre)-(cre>other)-(other>emo) 0.02760736 0.03020347
## 6 (other>emo)-(emo>other)-(emo>other) 0.02760736 0.03065596
## 7 (other>log) 0.21625767 0.03181114
## 8 (other>cre)-(cre>other)-(emo>other) 0.01687117 0.03789266
## 9 (cre>other)-(other>emo) 0.06134969 0.03881494
## 10 (other)-(other>emo)-(emo>cre) 0.06134969 0.03881494
## statistic index Freq.no donation Freq.donation Resid.no donation
## 1 5.983339 85 0.020689655 0.06353591 -1.920869
## 2 5.828109 230 0.003448276 0.03314917 -1.988757
## 3 5.319177 100 0.017241379 0.05524862 -1.835186
## 4 4.748647 94 0.020689655 0.05801105 -1.734043
## 5 4.697674 154 0.010344828 0.04143646 -1.769258
## 6 4.672126 157 0.044827586 0.01381215 1.764921
## 7 4.608646 12 0.175862069 0.24861878 -1.479269
## 8 4.309834 306 0.003448276 0.02762431 -1.759837
## 9 4.268949 58 0.037931034 0.08011050 -1.610107
## 10 4.268949 60 0.037931034 0.08011050 -1.610107
## Resid.donation
## 1 1.719263
## 2 1.780026
## 3 1.642573
## 4 1.552046
## 5 1.583565
## 6 -1.579683
## 7 1.324012
## 8 1.575133
## 9 1.441118
## 10 1.441118
##
## Computed on 652 event sequences
## Constraint Value
## max.gap 3
## count.method COBJplot(discrcohort11_1, resid.levels = c(0.05,0.01), rows = 1, cols = 1)
plot(discrcohort11_1, resid.levels = c(0.05,0.01), rows = 1, cols = 1, ptype = "resid")
persuader <- read.csv("persuaderPersuadee.csv", stringsAsFactors = F)
persuader.alphab <- c("opening","askp","providep","positive","offtask","asktask","agree","disagree","cre","emo","log","other","pro","task") # the order of strategies
#persuader.alphab <- c("A1","A11","A12","A16","A17","A18","A19", "A3", "A5","A6","cre","emo","log","other","pro","task") # the order of strategies
persuader.seq <- seqdef(persuader, 3:22, alphabet = persuader.alphab)## [>] 14 distinct states appear in the data:## 1 = agree## 2 = askp## 3 = asktask## 4 = cre## 5 = disagree## 6 = emo## 7 = log## 8 = offtask## 9 = opening## 10 = other## 11 = positive## 12 = pro## ...## Warning: [!] No automatic color palette assigned because number of states > 12.
##
## Use 'cpal' argument to assign one.## [>] state coding:## [alphabet] [label] [long label]## 1 opening opening opening## 2 askp askp askp## 3 providep providep providep## 4 positive positive positive## 5 offtask offtask offtask## 6 asktask asktask asktask## 7 agree agree agree## 8 disagree disagree disagree## 9 cre cre cre## 10 emo emo emo## 11 log log log## 12 other other other## ... (14 states)## [>] no color palette attributed, provide one to use graphical functions## [>] 633 sequences in the data set## [>] min/max sequence length: 20/20donate.seq <- seqdef(persuader %>% filter(persuader$donate_p==1), 3:22, alphabet = persuader.alphab)## [>] 14 distinct states appear in the data:## 1 = agree## 2 = askp## 3 = asktask## 4 = cre## 5 = disagree## 6 = emo## 7 = log## 8 = offtask## 9 = opening## 10 = other## 11 = positive## 12 = pro## ...## Warning: [!] No automatic color palette assigned because number of states > 12.
##
## Use 'cpal' argument to assign one.## [>] state coding:## [alphabet] [label] [long label]## 1 opening opening opening## 2 askp askp askp## 3 providep providep providep## 4 positive positive positive## 5 offtask offtask offtask## 6 asktask asktask asktask## 7 agree agree agree## 8 disagree disagree disagree## 9 cre cre cre## 10 emo emo emo## 11 log log log## 12 other other other## ... (14 states)## [>] no color palette attributed, provide one to use graphical functions## [>] 355 sequences in the data set## [>] min/max sequence length: 20/20notdonate.seq <- seqdef(persuader %>% filter(persuader$donate_p==0), 3:22, alphabet = persuader.alphab)## [>] 14 distinct states appear in the data:## 1 = agree## 2 = askp## 3 = asktask## 4 = cre## 5 = disagree## 6 = emo## 7 = log## 8 = offtask## 9 = opening## 10 = other## 11 = positive## 12 = pro## ...## Warning: [!] No automatic color palette assigned because number of states > 12.
##
## Use 'cpal' argument to assign one.## [>] state coding:## [alphabet] [label] [long label]## 1 opening opening opening## 2 askp askp askp## 3 providep providep providep## 4 positive positive positive## 5 offtask offtask offtask## 6 asktask asktask asktask## 7 agree agree agree## 8 disagree disagree disagree## 9 cre cre cre## 10 emo emo emo## 11 log log log## 12 other other other## ... (14 states)## [>] no color palette attributed, provide one to use graphical functions## [>] 278 sequences in the data set## [>] min/max sequence length: 20/20#seqdplot(persuader.seq, group = persuader$donate_p, border = NA)#transition <- seqetm(persuader.seq, method = "transition")
#transition
pder.seqe <- seqecreate(persuader.seq)
pder.seqestate <- seqecreate(persuader.seq, tevent = "state")
pder.seqeperiod <- seqecreate(persuader.seq, tevent = "period")
#pder.seqe[1]
#pder.seqestate[1]
#pder.seqeperiod[1]
don.seqe <- seqecreate(donate.seq)
don.seqestate <- seqecreate(donate.seq, tevent = "state")
don.seqeperiod <- seqecreate(donate.seq, tevent = "period")
ndon.seqe <- seqecreate(notdonate.seq)
ndon.seqestate <- seqecreate(notdonate.seq, tevent = "state")
ndon.seqeperiod <- seqecreate(notdonate.seq, tevent = "period")pdersubseq <- seqefsub(pder.seqestate, min.support = 10, constraint = seqeconstraint(count.method = 1, max.gap = 1))
cohort <- factor(persuader$donate_p > 0, labels = c("no donation", "donation"))
discrcohort011 <- seqecmpgroup(pdersubseq, group = cohort, method = "chisq", pvalue.limit = 0.05)
discrcohort011## Subsequence Support p.value
## 1 (disagree)-(log) 0.02211690 0.0005424183
## 2 (agree) 0.61295419 0.0018844968
## 3 (other)-(offtask)-(other)-(providep) 0.08056872 0.0029536263
## 4 (emo)-(offtask)-(cre)-(providep) 0.02053712 0.0068264313
## 5 (other)-(offtask)-(other)-(providep)-(other) 0.04265403 0.0084769515
## 6 (log)-(asktask) 0.18799368 0.0088961488
## 7 (log) 0.68562401 0.0091766937
## 8 (log)-(asktask)-(log) 0.03791469 0.0113134393
## 9 (log)-(disagree) 0.02685624 0.0126378079
## 10 (disagree) 0.14691943 0.0159224466
## 11 (agree)-(log)-(agree) 0.03633491 0.0165184978
## 12 (providep)-(emo)-(offtask)-(emo) 0.07740916 0.0167769684
## 13 (agree)-(other)-(providep)-(other) 0.04107425 0.0169211694
## 14 (cre)-(agree)-(other) 0.02053712 0.0174581539
## 15 (other)-(positive) 0.17851501 0.0199605538
## 16 (agree)-(log)-(asktask) 0.02527646 0.0209025457
## 17 (agree)-(other)-(providep) 0.11374408 0.0214095592
## 18 (positive) 0.33807267 0.0224382848
## 19 (offtask)-(log)-(agree) 0.03475513 0.0240024339
## 20 (emo)-(providep)-(emo)-(offtask)-(emo) 0.03791469 0.0375483077
## 21 (asktask) 0.76777251 0.0379770102
## 22 (cre)-(asktask)-(emo)-(providep) 0.04107425 0.0403119619
## 23 (log)-(asktask)-(emo)-(providep) 0.01737757 0.0412021030
## 24 (agree)-(log) 0.17219589 0.0468448358
## 25 (other)-(agree)-(other)-(providep) 0.02685624 0.0494414029
## statistic index Freq.no donation Freq.donation Resid.no donation
## 1 11.963879 525 0.046762590 0.002816901 2.763129
## 2 9.658771 11 0.543165468 0.667605634 -1.486256
## 3 8.835896 142 0.118705036 0.050704225 2.240154
## 4 7.318080 571 0.039568345 0.005633803 2.214212
## 5 6.929835 282 0.068345324 0.022535211 2.074094
## 6 6.843558 53 0.140287770 0.225352113 -1.834522
## 7 6.788111 7 0.629496403 0.729577465 -1.130202
## 8 6.415407 313 0.014388489 0.056338028 -2.014515
## 9 6.219118 436 0.046762590 0.011267606 2.025310
## 10 5.811418 75 0.187050360 0.115492958 1.745669
## 11 5.746824 320 0.014388489 0.053521127 -1.919659
## 12 5.719556 148 0.107913669 0.053521127 1.828059
## 13 5.704532 286 0.017985612 0.059154930 -1.899485
## 14 5.649721 564 0.003597122 0.033802817 -1.970907
## 15 5.415341 57 0.136690647 0.211267606 -1.650495
## 16 5.334899 453 0.007194245 0.039436620 -1.896339
## 17 5.293142 98 0.079136691 0.140845070 -1.710909
## 18 5.211475 31 0.287769784 0.377464789 -1.442482
## 19 5.094457 340 0.014388489 0.050704225 -1.821514
## 20 4.325367 312 0.057553957 0.022535211 1.681682
## 21 4.306052 5 0.726618705 0.800000000 -0.783098
## 22 4.204706 288 0.061151079 0.025352113 1.651706
## 23 4.167683 694 0.003597122 0.028169014 -1.742975
## 24 3.950953 61 0.136690647 0.200000000 -1.426604
## 25 3.860303 444 0.010791367 0.039436620 -1.634471
## Resid.donation
## 1 -2.4451718
## 2 1.3152304
## 3 -1.9823764
## 4 -1.9594196
## 5 -1.8354248
## 6 1.6234211
## 7 1.0001477
## 8 1.7827017
## 9 -1.7922544
## 10 -1.5447927
## 11 1.6987611
## 12 -1.6177017
## 13 1.6809087
## 14 1.7441116
## 15 1.4605706
## 16 1.6781244
## 17 1.5140319
## 18 1.2764931
## 19 1.6119094
## 20 -1.4881684
## 21 0.6929858
## 22 -1.4616417
## 23 1.5424084
## 24 1.2624429
## 25 1.4463897
##
## Computed on 633 event sequences
## Constraint Value
## max.gap 1
## count.method COBJplot(discrcohort011, resid.levels = c(0.05,0.01), rows = 1, cols = 1)
plot(discrcohort011, resid.levels = c(0.05,0.01), rows = 1, cols = 1, ptype = "resid")
pdersubseq <- seqefsub(pder.seqe, min.support = 10, constraint = seqeconstraint(count.method = 1, max.gap = 1))
cohort <- factor(persuader$donate_p > 0, labels = c("no donation", "donation"))
discrcohort111 <- seqecmpgroup(pdersubseq, group = cohort, method = "chisq", pvalue.limit = 0.05)
discrcohort111## Subsequence
## 1 (disagree>log)
## 2 (other>offtask)-(offtask>other)-(other>providep)
## 3 (emo>offtask)-(offtask>cre)-(cre>providep)
## 4 (other>offtask)-(offtask>other)-(other>providep)-(providep>other)
## 5 (log>asktask)
## 6 (log>asktask)-(asktask>log)
## 7 (log>disagree)
## 8 (agree>log)-(log>agree)
## 9 (providep>emo)-(emo>offtask)-(offtask>emo)
## 10 (agree>other)-(other>providep)-(providep>other)
## 11 (cre>agree)-(agree>other)
## 12 (other>positive)
## 13 (agree>log)-(log>asktask)
## 14 (agree>other)-(other>providep)
## 15 (offtask>log)-(log>agree)
## 16 (emo>providep)-(providep>emo)-(emo>offtask)-(offtask>emo)
## 17 (cre>asktask)-(asktask>emo)-(emo>providep)
## 18 (log>asktask)-(asktask>emo)-(emo>providep)
## 19 (agree>log)
## 20 (other>agree)-(agree>other)-(other>providep)
## Support p.value statistic index Freq.no donation Freq.donation
## 1 0.02211690 0.0005424183 11.963879 544 0.046762590 0.002816901
## 2 0.08056872 0.0029536263 8.835896 135 0.118705036 0.050704225
## 3 0.02053712 0.0068264313 7.318080 593 0.039568345 0.005633803
## 4 0.04265403 0.0084769515 6.929835 286 0.068345324 0.022535211
## 5 0.18799368 0.0088961488 6.843558 45 0.140287770 0.225352113
## 6 0.03791469 0.0113134393 6.415407 320 0.014388489 0.056338028
## 7 0.02685624 0.0126378079 6.219118 450 0.046762590 0.011267606
## 8 0.03633491 0.0165184978 5.746824 327 0.014388489 0.053521127
## 9 0.07740916 0.0167769684 5.719556 143 0.107913669 0.053521127
## 10 0.04107425 0.0169211694 5.704532 290 0.017985612 0.059154930
## 11 0.02053712 0.0174581539 5.649721 586 0.003597122 0.033802817
## 12 0.17851501 0.0199605538 5.415341 49 0.136690647 0.211267606
## 13 0.02527646 0.0209025457 5.334899 468 0.007194245 0.039436620
## 14 0.11374408 0.0214095592 5.293142 89 0.079136691 0.140845070
## 15 0.03475513 0.0240024339 5.094457 348 0.014388489 0.050704225
## 16 0.03791469 0.0375483077 4.325367 318 0.057553957 0.022535211
## 17 0.04107425 0.0403119619 4.204706 292 0.061151079 0.025352113
## 18 0.01737757 0.0412021030 4.167683 728 0.003597122 0.028169014
## 19 0.17219589 0.0468448358 3.950953 53 0.136690647 0.200000000
## 20 0.02685624 0.0494414029 3.860303 459 0.010791367 0.039436620
## Resid.no donation Resid.donation
## 1 2.763129 -2.445172
## 2 2.240154 -1.982376
## 3 2.214212 -1.959420
## 4 2.074094 -1.835425
## 5 -1.834522 1.623421
## 6 -2.014515 1.782702
## 7 2.025310 -1.792254
## 8 -1.919659 1.698761
## 9 1.828059 -1.617702
## 10 -1.899485 1.680909
## 11 -1.970907 1.744112
## 12 -1.650495 1.460571
## 13 -1.896339 1.678124
## 14 -1.710909 1.514032
## 15 -1.821514 1.611909
## 16 1.681682 -1.488168
## 17 1.651706 -1.461642
## 18 -1.742975 1.542408
## 19 -1.426604 1.262443
## 20 -1.634471 1.446390
##
## Computed on 633 event sequences
## Constraint Value
## max.gap 1
## count.method COBJplot(discrcohort111, resid.levels = c(0.05,0.01), rows = 1, cols = 1)
plot(discrcohort111, resid.levels = c(0.05,0.01), rows = 1, cols = 1, ptype = "resid")
